Explainable AI [1]} is the problem of interpreting, understanding and visualizing machine learning models. Deep convolutional network trained at large scales are traditionally considered blackbox systems, where designers have an understanding of the dataset and loss functions for training, but limited understanding of the learned model. Furthermore, predictions generated by the system are often not explainable as to why the system generated this output for that input. An explainable AI system would enable interpretation of what the ML model has learned [2]}[3]}, enable transparency to understand and identify biases or failure modes in the system [4]}[5]}[6]}[7]} and provide user friendly visualizations to build user trust in critical applications [8]}[9]}[10]}.
